The physical world contains many complex materials whose reflectance properties need to be modeled or captured to produce realistic computer graphics imagery. The effects of spatial variation and anisotropy of such materials can be represented by the six-dimensional Spatially Varying Bidirectional Reflectance Distribution Function (SVBRDF), but are particularly challenging to reproduce. Traditional approaches represent the BRDF (Bidirectional Reflectance Distribution Function) at each surface point using parametric models.
Simple parametric models are unable to realistically capture the reflectance properties of many real-world materials. More complicated, multi-lobe models may represent any BRDF to an arbitrary accuracy, but their many parameters are very difficult to specify and control.
As a result, SVBRDF measurement systems have been employed to measure six-dimensional SVBRDFs directly from surface samples. However, the high dimensionality of SVBRDFs necessitates lengthy capture processes, using expensive devices that densely scan the light and view directions, and are thus impractical for many applications. Alternatively, brute-force acquisition techniques may be employed, but they also require precise calibration of multiple and/or moving cameras, adding to the cost and fragility of such measurement systems.